Assessing the Risk of Multiple SARS-CoV-2 Reinfections
Author Information
Author(s): Lombard Belinda, Cohen Cheryl, von Gottberg Anne, Dushoff Jonathan, Pulliam Juliet R. C., van Schalkwyk Cari
Primary Institution: South African DSI-NRF Centre of Excellence in Epidemiological Modelling and Analysis (SACEMA), Stellenbosch University, Stellenbosch, South Africa
Hypothesis
Can a generalised catalytic model effectively assess the population-level risk of multiple reinfections with SARS-CoV-2?
Conclusion
The model successfully detected increases in the risk of third infections, indicating no further increase beyond the Omicron wave.
Supporting Evidence
- The model was adapted to detect increases in the risk of nth infections.
- Simulation-based validation showed the model's robustness in detecting increases in the risk of third infections.
- No additional increase in the risk of third infection was detected after the Omicron wave.
Takeaway
This study created a model to help understand how likely people are to get reinfected with COVID-19 multiple times, especially after the Omicron variant.
Methodology
The study used a Bayesian approach to fit a catalytic model to the number of nth infections occurring at least 90 days after a previous infection.
Limitations
The model is validated specifically for third infections, and its applicability for four or more infections is unconfirmed; it is also sensitive to low counts of nth infections.
Digital Object Identifier (DOI)
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